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Cities, skills, and inequality


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Cities, Skills, and Inequality

Christopher H. Wheeler

Working Paper 2004-020A


September 2004


Research Division

411 Locust Street

St. Louis, MO 63102

______________________________________________________________________________________ The views expressed are those of the individual authors and do not necessarily reflect official positions of the Federal Reserve Bank of St. Louis, the Federal Reserve System, or the Board of Governors.

Federal Reserve Bank of St. Louis Working Papers are preliminary materials circulated to stimulate discussion and critical comment. References in publications to Federal Reserve Bank of St. Louis Working Papers (other than an acknowledgment that the writer has had access to unpublished material) should be cleared with the author or authors.


Cities, Skills, and Inequality

Christopher H. Wheeler Federal Reserve Bank of St. Louis

Research Division 411 Locust Street St. Louis, MO 63102 Christopher.H.Wheeler@stls.frb.org July 23, 2004 Abstract

The surge in U.S. wage inequality over the past several decades is now commonly attributed to an increase in the returns paid to skill. Although theories differ with respect to why, specifically, this increase has come about, many agree that it is strongly tied to the increase in the relative supply of skilled (i.e. highly educated) workers in the U.S. labor market. A greater supply of skilled labor, for example, may have induced skill-biased technological change or generated greater stratification of workers by skill across firms or jobs. Given that metropolitan areas in the U.S. have long possessed more educated populations than non-metropolitan areas, these theories suggest that the rise in both the returns to skill and wage inequality should have been particularly pronounced in cities. Evidence from the U.S. Census over the period 1950 to 1990 sup-ports both implications.




The rise in U.S. wage inequality over the past several decades has been the subject of a

massive body of research in recent years.1 Although many elements have been identified,

including changes in industrial structure (e.g. declining manufacturing) and institutional

factors (e.g. declining unionization and minimum wage), much of the literature has

con-cluded that the rise in the returns to skill, both observable and unobservable, has played a

major role in widening inequality.

At the same time, the literature studying urban labor markets has demonstrated (at

least recently) that, although workers situated in metropolitan areas tend to earn higher

wages than workers with the same observable characteristics living outside of cities (i.e.

there is a significant urban wage premium), the estimated premium is higher for workers

with greater measures of observable skill. In particular, Glaeser and Mare (2001) have

shown that workers with either more experience or more education receive a greater boost

from living in a metropolitan area than identical workers with less experience or education.

Thus, as they conclude (p. 340), “the urban wage premium is highest among the most

skilled workers.”

To date, of course, these two insights have largely been treated separately. Studies of

inequality have not, for the most part, considered the potential influence of urbanization

patterns on wage dispersion, and, although a large literature has studied the connection

between geographic concentration and aggregate growth and productivity (e.g. Carlino and

Voith (1992), Ciccone and Hall (1996), Henderson (1999)), surprisingly little work in urban

economics has considered how cities affect the distribution of economic outcomes across



This paper attempts to bridge these two literatures by offering some descriptive evidence

on wage inequality and returns to observable measures of skill for both the metropolitan

areas and non-metropolitan areas of the U.S. between 1950 and 1990. Results from five

Census samples over this period indicate that, although inequality in weekly wage and

salary earnings – measured by differences in the 90th, 50th, and 10th percentiles – increased

throughout the entire U.S., the increase was substantially higher within urban areas than

outside of them.

In 1950, for instance, inequality among white males between 18 and 65 years of age was

higher outside of metropolitan areas than in them. Rural 90-10, 90-50, and 50-10 log wage

differentials at this time exceeded those among urban residents by, respectively, 23, 6, and

17 percentage points.2 These gaps, however, had been virtually eliminated by 1970 and, by

1990, 90-10, 90-50, and 50-10 differences in urban areas exceeded those of the rural U.S. by

16, 5, and 11 percentage points.

Estimates of the returns to observable measures of skill (i.e. education and

experi-ence) indicate that, just as previous work has established, inequality between workers with

different levels of education (holding experience constant) and experience (holding

educa-tion constant) has grown steadily since 1950. The gap between educaeduca-tion groups within

metropolitan areas, however, has outpaced that among rural workers over this period. For

example, between 1950 and 1990, the wage gap between a worker with a college degree or

more and an observationally equivalent worker with only a high school degree increased by

9 percentage points for workers outside of metropolitan areas. Among urban workers, the

gap increased by 18 percentage points.

A similar result does not hold, however, when the returns to experience are considered.


To be sure, the gap between identical workers differing with respect to years of experience

has grown larger over time. A worker with between 26 and 30 years of experience earned 40

percent more, on average, than an identical worker with 0 to 5 years of experience in 1950.

By 1990, the premium had risen to 61 percent. In addition, those premia are significantly

higher in urban areas than in rural ones. Between 1950 and 1990, this particular premium

was, approximately 11 percentage points higher in cities than outside of them. Yet, the

results indicate that experience premia have risen by approximately the same amount within

urban areas as they have in rural ones and, thus, left the urban-rural difference relatively

constant over time.

Such findings, I believe, are interesting because they provide an indication as to how the

spatial distribution of the U.S. population across urban and rural areas may have influenced

the degree of inequality in the labor market. In particular, one of the most striking trends

characterizing the geographic distribution of the U.S. population over the last century has

been increasing urbanization. Black and Henderson (1999), for example, report that the

fraction of the population residing in metropolitan areas increased from roughly 40 to 60

percent between 1900 and 1950. The data on white males between the ages of 18 and 65

used in the analysis here indicates a similar rate of increase between 1950 and 1990, when

the urbanization rate rose from 64.5 percent to 78.6 percent.

Combining this trend with the pattern describing the returns to experience yields the

following insight. Given that experience premia have been consistently higher in

metropoli-tan areas than outside of them over the sample period, growing urbanization has likely

contributed to increased inequality through a cross-sectional effect. That is, as the fraction

of workers residing in urban areas has increased, the degree of spread between the wage


ex-perience premia have also increased over the sample period, the increase has been similar

in both rural and urban areas. With respect to experience premia, then, urbanization has

contributed to inequality through a level effect (i.e. shifting population to areas with higher

inequality levels) as opposed to a growth rate effect (i.e. shifting populations to areas with

higher inequality growth).

With education, on the other hand, the movement of the population from rural to

urban areas has likely added to inequality because, over time, the returns to educational

attainment in cities has increased beyond that in rural areas. Thus, because education

premia in cities did not differ substantially from those in rural areas in 1950, 1960, or

even 1970, an increase in the degree of urbanization would not have generated much of an

increase in between-education-group inequality over these years. When combined with the

significant rise in education premia in urban areas in 1980 and 1990, however, increased

urbanization likely exacerbated inequality levels. The impact of urbanization on inequality

due to education returns, therefore, has both cross-sectional and temporal aspects.

The remainder of the paper proceeds as follows. The next section offers a brief summary

of two well-known theories of inequality, both of which suggest that wage inequality may

have increased more rapidly inside of cities than outside of them. Section 3 then describes

the data used in the analysis, the results from which appear in Sections 4, 5, and 6. A formal

decomposition of changing aggregate U.S. inequality into urban and rural observables and

unobservables is given in Section 7. Section 8 concludes.


Background: Theories of Skill Returns and Inequality

There are at least two prominent explanations for the rise of U.S. wage inequality which


inside of cities than outside of them. First, as argued by Acemoglu (1998, 2002),

technologi-cal change may have been skill-biased in recent decades, allowing both the productivity and

earnings of high-skill workers to rise relative to low-skill workers. This change, he argues,

is largely the product of a rise in the relative supply of skilled workers in the U.S. labor

force over the past several decades. Indeed, in 1950, a mere 15.4 percent of employed white

males between the ages of 18 and 65 had completed some education at the college level. By

1990, this figure had more than tripled, reaching 54.2 percent.3 Such a rise has, at least in

theory, made the search for workers possessing a high level of skill easier and, consequently,

increased the expected payoff to investing in skill-complementing technologies.

As it happens, much of this rise in educational attainment has been concentrated in

cities.4 Consider Table 1A, which reports the distribution of educational attainment for

white male workers between the ages of 18 and 65 for each of the five Census years between

1950 and 1990. In the table, there are two sets of statistics across the five years: one based

on the population residing inside of metropolitan areas (i.e. urban), the other calculated

from the population of individuals living outside of metroplitan areas (i.e. rural).

Quite clearly, metropolitan areas have been characterized by greater educational

attain-ment than rural ones over this period. In each each year, the fraction of white males with

at least some education at the college level was higher in cities than outside of them.

More-over, this urban-rural difference has grown over time. In 1950, 17 percent of metropolitan

area workers had completed at least some college versus 12 percent for non-metropolitan

3These figures are calculated from the U.S. Census samples described in the next section. The only

selection criteria applied in calculating these statistics is that individuals report positive wage and salary earnings.

4Throughout the paper, I use the terms ‘city’ and ‘metropolitan area’ interchangeably for expositional


areas. By 1990, these figures had risen to 57 percent among urban dwellers as opposed to

42 percent for rural residents. Table 1B demonstrates that much the same pattern holds

when each of the four Census regions is considered separately, so this aggregate trend is not

being driven solely by cities in one part of the country.5

If technologies do, in fact, respond to the distribution of skill, these educational

at-tainment patterns suggest that skill-biased technological change should have been most

pronounced in urban areas where the relative supply of skilled workers has been the

high-est. We should then expect to see a particularly rapid increase in the returns to skill and

wage inequality within metropolitan areas.

Interestingly, urban theory has long argued that cities are an important source of growth

and innovation due to the enhanced exchange of ideas and knowledge that arises from a

dense spatial configuration of economic agents (e.g. Marshall (1920), Jacobs (1969), Lucas

(1988), Duranton and Puga (2001)). Empirical evidence certainly offers some support for

this hypothesis. Harrison et al. (1996), for example, find that plants located in large urban

markets are more likely to adopt new technologies than those located in smaller markets.

Similarly, Feldman and Audretsch (1996) find that the development of new products tends

to be clustered in large, diverse urban areas. As a result, the combination of more skilled

populations with dense environments may make urban areas a focal point for skill-biased

technological change and, thus, earnings inequality.6

5Constituent states for each region are listed in the Appendix.

6Although the literature examining innovation in cities has focused on whether localization (i.e. the

concentration of a single industry) or urbanization (i.e. diversity) effects are more important for technological change, little work has considered the role of human capital. Yet, since much of the evidence indicates that innovation is more prevalent in large, diverse urban markets (e.g. Glaeser et al. (1992), Harrison et al. (1996), Feldman and Audretsch (1996)), which also tend to have large supplies of skilled workers, innovation may be driven by skill distributions, not necessarily by diversity per se.


A second explanation for rising skill returns and wage dispersion focuses instead on the

manner in which workers are organized into production units. In particular, the theory

advanced by Kremer and Maskin (1996) suggests that both can be linked to an increase in

the extent to which workers are segregated by skill at the workplace (i.e. high-skill workers

working with other high-skill workers leaving low-skill workers to work amongst themselves).

Assuming that skills are complementary in production, increases in segregation magnify

the earnings of individuals at the top end of the skill distribution while decreasing those of

individuals at the bottom.

Although Kremer and Maskin’s (1996) analysis examines a frictionless environment in

which increased segregation is driven by a widening of the underlying skill distribution,

it is straightforward to show that the same basic result emerges in a search-based model

when search costs decrease.7 Because urban areas may involve lower search costs, say

due to a greater flow of information between workers and producers or, alternatively, by

allowing inefficient matches to be more readily replaced by productive ones, cities may be

characterized by more extensive segregation.

Over time, these higher returns may have produced more rapid changes in

inequal-ity as the most talented workers from the national (or even international) economy have

been drawn to urban labor markets. Such an influx, after all, would shift the upper tails

of the earnings distributions in metropolitan areas to the right.8 This hypothesis is not

incompatible with the educational attainment statistics reported in Tables 1A and 1B.9

7See Wheeler (2001) for a characterization of a simple search environment. 8I thank an anonymous referee for making this point.

9As noted by Glaeser et al. (2000), low-wage workers may also tend to flock to cities to take advantage

of various urban amenities (e.g. transportation) thereby generating greater wage dispersion in cities. Of course, this would not necessarily imply that cities experience more rapid changes in inequality unless urban amenities have become increasingly desirable among low-wage workers over time (i.e. relative to high-wage




The data are derived from the following five 1 Percent Census samples of the Integrated

Public Use Microdata Series (IPUMS) compiled by Ruggles and Sobek et al. (2003): 1950

General Sample, the 1960 General Sample, the 1970 Form 2 State Sample, the 1980 Metro

(‘B’) Sample, and the 1990 Metro Sample.10 These data are selected because they provide

larger numbers of observations over a longer time horizon than other data sets commonly

used to examine inequality, such as the Current Population Survey. In addition, since my

goal is to investigate general inequality patterns and not to identify specific years in which

inequality may have started a particular short-run or long-run trend, I use the Census.

With each sample, I begin by eliminating all observations except those for white males

between the ages of 18 and 65 who reported positive wage and salary earnings for the

year and for whom both metropolitan area status (i.e. whether an individual lived in a

metropolitan area or not) and state of residence are reported.11 Doing so generates sample

sizes of 87814 for 1950, 290558 for 1960, 365897 for 1970, 478302 for 1980, and 517768 for

1990.12 These are then used to calculate the educational attainment distributions reported

in Tables 1A and 1B.

workers). This conjecture, however, seems difficult to reconcile with Tables 1A and 1B.

10To construct inequality measures for specific metropolitan areas (as in Sections 4 and 6), I also use the

1970 Form 2 Metro Sample.

11Metropolitan area definitions do change from one Census year to the next (existing areas expand, new

ones emerge), so the analysis is not (necessarily) based on a consistent set of geographic entities. While this feature of the data may influence some of the results (e.g. rural areas may lose high-wage suburbs to urban areas over time), I argue below that it does not likely account entirely for the patterns reported. See footnotes 14 and 19.

12In part, the 1950 sample is considerably smaller than all of the other samples because several key


When estimating either wage inequality or skill returns, I further restrict these samples

to those individuals who worked at least 14 weeks in the past year, were not in school at

the time of interview, and earned at least 67 dollars per week (in 1982 dollars). These are

standard selection criteria in many existing studies of inequality (e.g. Katz and Murphy

(1992), Juhn et al. (1993), Bernard and Jensen (2000)). Doing so leaves 76825 observations

for 1950, 258993 for 1960, 311987 for 1970, 403326 for 1980, and 433350 for 1990.

Topcoded wage and salary earnings in each year’s sample are imputed as 1.5 times the

topcode, with the exception of 1990 in which the topcoded value is estimated as 210,000

dollars. This is similar to the scheme utilized by Autor et al. (1998) and Acemoglu and

Angrist (1999) who also study labor earnings using Census samples. All dollar figures are

converted to real terms using the Personal Consumer Expenditure Chain-Type Price Index

of the National Income and Product Accounts.

The wage measure examined throughout the analysis is a worker’s weekly wage,

cal-culated as the ratio of annual real wage and salary earnings to weeks worked. Because

weeks worked is reported in categorical form in both the 1960 and 1970 Census samples,

weeks worked for these years are estimated as follows. First, I divide the 1980 and 1990

sample observations by weeks worked categories corresponding to the categories reported

in the 1960 and 1970 samples. Within each category, I further divide the observations by

educational attainment (no high school, some high school, high school only, some college,

college degree or higher). I then calculate a mean number of weeks worked for each group

and assign the average of the 1980 and 1990 means to the corresponding groups in the 1960

and 1970 samples.

Finally, although the 1960, 1970, and 1980 Census samples are constructed as random


and 1990 samples in the analysis below are weighted using, respectively, the sample line

and person weights reported by the IPUMS.


Overall Inequality Patterns

From the construction of log weekly wages for individuals in each of the five Census

sam-ples, I begin by calculating the 90th, 50th, and 10th percentiles of three distributions: (i)

all individuals, (ii) all individuals residing in metropolitan areas, and (iii) all individuals

residing outside of metropolitan areas. Inequality is then measured for each of these samples

as 90-10, 90-50, and 50-10 differences. Results are reported in Table 2A.

Beginning with the distribution of log weekly wages throughout the entire U.S., we see

the usual result: there was a substantial rise in wage differentials at both the top and bottom

of the distribution. Between 1950 and 1990, 90-50 differences increased by 25 percentage

points; 50-10 differences increased by 31 percentage points, implying that the difference

between the 90th and 10th percentiles increased by approximately 56 percentage points

over this period. Much of this overall rise, not surprisingly, occurred during the 1980s,

when the 90-10 differential increased by 25 percentage points alone. Again, this much has

already been established by previous research.

When the sample is divided into metropolitan and non-metropolitan residents, however,

we can see a substantial difference in the trends in inequality. Although both urban and

rural inequality measures increased over this 40-year period, metropolitan areas exhibited

a much more striking increase. In 1950, each of the inequality measures was actually larger

outside of cities than within them. Among rural residents in 1950, the 90th percentile was

119 percent higher than the 10th percentile. For cities, the figure was a more modest 96


and the 10th percentile, which was 17 percentage points higher in rural areas than in urban


The gap between urban and rural inequality, however, declined steadily over the next

three decades until 1980, at which point 90-10 and 50-10 differences in metropolitan areas

had overtaken those outside of cities. During the 1980s, each of the three inequality measures

continued to increase more rapidly in U.S. cities than outside of them: 90-10 differences

increased by 23 percentage points, 90-50 by 13 percentage points, and 50-10 by 10 percentage

points. The corresponding changes among non-metropolitan residents were 14, 8, and 7. As

a consequence, by 1990, wage inequality was substantially higher within urban areas than

outside of them, a feature that stands in direct contrast to what existed in 1950.

To see that these results are not being driven merely by inter-regional differences in

wage earnings across cities (e.g. extremely large, dense urban areas in the Northeast

ver-sus smaller, less dense urban areas in the South), consider first Table 2B, which reports

inequality measures calculated separately for each of the four Census regions (West,

Mid-west, Northeast, and South). Although levels of inequality across the four regions differ

somewhat over this time period – the South and West appear to have the highest inequality

measures, the Midwest the lowest – the same basic pattern is present in all four. With

the exception of the 90-50 difference in the Northeast region at the beginning of the sample

period, rural inequality exceeded that found in urban areas in 1950, whereas by 1990, urban

inequality had become the larger of the two. During this time frame, for example, 90-10

differences increased in the urban areas of the West, Midwest, Northeast, and South regions

by, respectively, 73, 68, 65, and 56 percentage points. The corresponding rural figures were

37, 33, 42, and 27.


from a collection of individual metropolitan areas over the period 1970 to 1990.13 For the

sake of highlighting the extent of inequality in urban areas, the table shows 90-10, 90-50,

and 50-10 wage differentials for the three largest metropolitan areas in the country (as of

1990 population) – New York, Los Angeles, Chicago – and the average levels across all

remaining metropolitan areas from each year’s sample.

Two features stand out. First, when compared to the increase in rural inequality in

the U.S., the average increase in inequality witnessed within specific metropolitan areas

was substantially higher. For example, although 90-10 wage differentials increased by 21

percentage points between 1970 and 1990 among rural workers, the (unweighted) average

within-city increase was roughly 34 percentage points.14 Therefore, the rapid growth in

urban inequality documented in Tables 2A and 2B does not seem to be entirely the product

of growing between-city differences in wages. Second, among cities, the increase in inequality

was particularly pronounced among the country’s largest urban areas. Over this period,

90-10 differentials in New York, Los Angeles, and Chicago registered increases of, respectively,

44, 54, and 47 percentage points as opposed to an average of 34 percentage points for the

remaining metropolitan areas.

The basic upshot of this evidence, quite simply, is that growing wage inequality in the

13The sample, in this case, is limited to a shorter time horizon because information on wage and salary

earnings in specific metropolitan areas is extremely limited for years prior to 1970. Metropolitan areas are defined as metropolitan statistical areas (MSAs) or consolidated metropolitan statistical areas (CMSAs).

14Because a similar pattern emerges within each decade individually – 1970-1980 and 1980-1990 – it is

unlikely that changes in the geographic definitions of urban and rural areas (noted in footnote 11) entirely account for these urban-rural gaps in inequality growth. In particular, definitional changes within a single decade tend to be small (see IPUMS documentation, Ruggles and Sobek et al. (2003)). Hence, even among specific metropolitan areas with relatively stable geographic definitions (e.g. Chicago between 1970 and 1980; Los Angeles over both decades), inequality has risen faster than in the rural U.S..


U.S. has a strong urban component. The next section considers the influence of skill returns

in helping to explain this result.


Returns to Observable Measures of Skill

Following the insights of the existing literature on wage inequality, an obvious candidate

explanation for the urban-rural difference in inequality trends is the return to skill. This

section provides evidence on the evolution of the returns to two observable measures of skill

– education and experience – across urban and rural workers between 1950 and 1990. To

this end, I consider the following statistical characterization of a worker’s wage earnings.

Let the logarithm of individual’si’s weekly wage in year t, wit, be given by

wit=αt+δt(Urbanit) +βtXit+γtZit+it (1)

where αt is an overall, time-specific constant; Urbanit is an urban residence indicator; and Xit is a vector of personal covariates including the number of weeks worked, marital status, three Census region dummies, nine digit occupation indicators, and nine

one-digit industry indicators.15 The vector Zit contains two observable measures of skill: four educational attainment dummies (no high school, some high school, some college, college or

more), and eight potential experience indicators (6-10 years, 11-15 years, 16-20 years, 21-25

15Occupations include Professional/Technical; Farmers; Managers, Officials, Proprietors; Clerical and

Kin-dred; Sales; Craftsmen; Operatives; Service; and Farm Laborers. Industries include Agriculture, Forestry, Fishing; Mining; Construction; Durable Manufacturing; Nondurable Manufacturing; Transportation, Com-munications, Other Utilities; Trade; FIRE; and Services.


years, 26-30 years, 31-35 years, 36-40 years, 40 or more years).16 The parameters, δt, βt, and γt, which capture the influence of these characteristics on wage earnings, are allowed to differ across years. The final term, it, is a person-time-specific residual allowed to be both heteroskedastic and cross-correlated within states.17

Consider, first, the results from the baseline specification of equation (1) whereby both

vectors of prices,βtandγt, are restricted to be constant across all individuals, regardless of urban-rural status, within a given year. In particular, this baseline case assumes that skill

prices,γt, do not differ between urban and rural residents although they may change over time. Estimates are presented in Table 3.

Looking across the rows to see the evolution over time, a couple of patterns are notable.

First, there is a substantial urban wage premium – roughly on the order of 12 to 14 percent

– which remained fairly constant between 1950 and 1990. That is, across all workers,

the average shift effect associated with urban residence has boosted weekly wages by a

reasonably constant factor – 12 to 14 percent – for the last half century.

Second, over the same period, there has been a rise in the return to observable measures

of skill, especially educational attainment. To be sure, the coefficient estimates, which

represent the average effects relative to workers with a high school degree only, suggest a

monotonic rise of average weekly wages with years of schooling completed. Yet, over time,

the differences in the wages of ‘identical’ workers with different years of schooling became

noticeably larger. The relative wage of white males with no high school (0 to 8 years of

16Potential experience is calculated as the maximum of (age - years of education - 6) and 0. Because the

1990 Census does not code education as years of schooling completed for all individuals, years of schooling are imputed using the figures reported in Table 5 of Park (1994).

17Estimation proceeds by OLS, but standard errors are adjusted in the spirit of White (1980). Results


schooling) dropped from -17 percent in 1950 to -30 percent by 1990. For those with a college

degree or more, the relative wage increased from 25 percent in 1950 to 42 percent in 1990.

Thus, there has been a rise in between-education-group inequality over this period.

The estimated returns to experience, which in Table 3 are reported as returns relative

to workers with only 0 to 5 years of experience, show a similar albeit less striking pattern.

Again, more experience is associated with higher wages, on average. In addition, for some

experience categories, particularly those representing more than 20 years, the estimated

premium also rose, more or less, over the sample period. When considering otherwise

identical workers, for example, an individual with 26 to 30 years of work experience made 40

percent more per week, on average, than a worker with between 0 and 5 years of experience

in 1950. By 1990, that difference had risen to 61 percent.

A more important question with respect to the issue at hand, however, is the following:

Do these skill premia vary with urban-rural status as suggested by the theories of wage

inequality mentioned previously? To answer this question, I estimate a second specification

of (1) in which the prices of observable skill,γt, are permitted to vary by urban-rural status within each year.18 Those results appear in Table 4.

Because I have interacted each skill variable with the urban status dummy, the

coef-ficients on the education variables now represent an individual’s wage relative to that of

an otherwise identical high-school graduate across rural residents. Likewise, the experience

coefficients in this case denote a worker’s weekly wage relative to that of a worker with

0 to 5 years of experience among non-metropolitan area dwellers. For urban residents,

these relative skill premia are given simply by the sums of these raw coefficients and the

18Naturally, I could permit the coefficients pricing ‘non-skill’ personal covariates, β

t, to vary by urban-rural status as well. However, since the goal of the analysis is to focus on urban-urban-rural differences in skill prices, I do not consider that case here.


corresponding interacted values.

With this interpretation in mind, it is interesting to note that, although the increase in

the gap between workers differing only by educational attainment has grown on average,

the increase has been much larger among urban dwellers than rural ones. Among rural

residents, for instance, the high school wage premium relative to workers with no high

school completed increased from 18 percent to 23 percent between 1950 and 1990, while

the college-high school premium increased from 22 percent to 31 percent. These figures are

considerably lower than what occurred in U.S. metropolitan areas where the high school-no

high school gap increased from 17 percent to 31 percent, and the college-high school gap

rose from 26 percent to 44 percent.

This result, incidentally, reveals another finding of interest. The urban wage premium,

which, according to the first specification described above, remained fairly steady at 12 to

14 percent between 1950 and 1990, has not been steady within education groups. In 1950,

the premium for workers with between 0 and 8 years of schooling completed was 6.5 percent.

That is, an urban resident with 0 to 8 years of education earned, on average, 6.5 percent

more per week than an identical worker living outside of a metropolitan area. For high

school graduates and those with a college degree or more, the premia were quite similar in

magnitude: respectively, 5.5 and 9.5 percent. While this same pattern held for the next two

decades, the urban premium started to diverge across education groups betweeen 1970 and

1980 so that by 1990, the situation had changed dramatically: the urban residence premia

for workers with no high school, high school only, and college or more were, respectively,

-4.6, 3.4, and 16.4 percent. Evidently, some fundamental change favoring highly educated

workers occurred in urban markets at this time.


has been a rise in the relative returns to experience among both urban and rural dwellers

over time, particularly for those workers with more than 20 years. In addition, the fact

that the interactions with the urban status dummy are significantly positive and generally

rise with experience indicates that the gap between the wages of workers belonging to

different experience categories tends to be larger in metropolitan areas than outside of them.

However, there is no discernible trend in these relative returns over time. Consider, again, a

worker with 26 to 30 years of work experience. Between 1950 and 1990, the rural premium

relative to a worker with 0 to 5 years of experience rose by approximately 20 percentage

points. Among urban residents, that same premium rose by 19 percentage points, leaving

the urban-rural difference reasonably constant.


Residual Inequality Patterns

To what extent do these changing returns to observable measures of skill account for the

trends in overall inequality documented in Section 4? One way to provide an answer,

nat-urally, is to consider the trends in residual inequality. This section provides some evidence

on the distribution of residual wages after a first-stage regression on a worker’s observable


In this case, I estimate a version of equation (1) in which the vector of personal

char-acteristics,Xit, is specified as before (weeks worked, marital status, three region dummies, nine occupation dummies, nine industry dummies), and the vector of skill prices in year

t, γt, is allowed to vary across urban and rural residents. In addition, although educa-tion entersZit as before in indicator-variable form, I replace the eight categorical dummies with a fourth-order polynomial in potential experience to account for any within-category


individ-ually for each year, I then construct 90-10, 90-50, and 50-10 differentials using the residual

distributions. Results for the United States as a whole are reported in Table 5A.

Most notably, they indicate that, although the covariates considered in the first stage

capture a sizable fraction of the increase in inequality across all three levels (i.e. total

U.S., urban U.S., rural U.S.) in the sense that the increase in the three residual inequality

measures is substantially less than the increase in the overall inequality measures, a large

fraction of the rise in U.S. wage inequality is not tied directly to observable characteristics.

Indeed, looking at Table 2A, we see that 90-10 differences in raw wages increased by 56

percentage points between 1950 and 1990 for the U.S. as a whole. Over the same period,

residual inequality increased by 34 percentage points, suggesting that more than half of

overall inequality is tied to unobserved elements.

Since numerous authors (e.g. Juhn et al. (1993) and Acemoglu (2002)) have suggested

that rising residual inequality still represents a rise in the premium paid to skills, do we find

that residual inequality has risen more rapidly in cities? The results in Table 5A indicate

that it has, at least to a modest extent. Much like with raw wage level differences, in 1950,

the difference between the 90th and 10th percentile of the residual log wage distribution was

higher outside of metropolitan areas than it was inside them: 0.94 versus 0.85. By 1990,

however, the opposite was the case. The 90-10 difference had risen to 1.23 within cities,

1.18 outside of them. The same qualitative pattern holds for 90-50 and 50-10 differences

as well, indicating that the distribution of residual wages has grown faster in cities than

outside of them at both the top and bottom of the distribution.19

19This may further help to mitigate concerns that changing city definitions completely influence the results.

In particular, if one is concerned that, as cities expand, they tend to become increasingly heterogeneous (i.e. inequality rises) while rural areas become more homogeneous (i.e. inequality falls), residual measures should at least offset the influence of changing observable characteristics (industry, occupation, education,


Results, again, are similar when the analysis is conducted separately by Census region

(Table 5B) and for individual metropolitan areas (Table 5C). While residual measures of

inequality all exhibit a significantly lower rate of increase over the sample period than the

overall inequality measures listed in Tables 2B and 2C, all show a substantial increase over

time. Therefore, much of the overall wage inequality witnessed within each region and

metropolitan area can be attributed to unobserved elements. More importantly, however,

the same basic conclusion remains: the rate of increase in residual inequality remains higher

among metropolitan area populations than among non-urban populations.


A Decomposition - Urban Versus Rural Components

To get a better sense of how much the change in overall U.S. inequality has been influenced

by urban and rural elements, consider the following decomposition based on the method of

Juhn et al. (1993). For each individuali in each year t, let

w1 it=Urbanit  ¯ α + ¯δ + ¯β ¯XU+ ¯γUZ¯U+ ¯U it  + (1− Urbanit)  ¯ α + ¯βXit+ ¯γRZit+ ¯Rit  (2)

where Urbanit is an indicator equal to 1 if the individual lives in a metropolitan area; Xit and Zit are vectors personal covariates (non-skill and skill); ¯XU and ¯ZU are vectors of average characteristics taken across all urban residents in all years; ¯γUγR) is the average coefficient vector on education and experience estimated for urban (rural) residents using

a single, pooled regression across all years; ¯α, ¯δ, and ¯β are the overall intercept, urban residence coefficient, and coefficient vector on non-skill covariates from this same pooled


regression; and ¯UitRit) represents the average urban (rural) residual for this worker based on his position in the urban (rural) residual distribution following estimation of equation

(1).20 Since only the characteristics of rural dwellers change from year to year, I interpret

changes in the distribution of w1 as those associated with rural characteristics. I then calculate w2 it=Urbanit  ¯ α + ¯δ + ¯βXit+ ¯γUZit+ ¯Uit  + (1− Urbanit)α + ¯¯ βXit+ ¯γRZit+ ¯Rit (3)

in which the characteristics of both urban and rural residents are permitted to change over

time. The contribution of changing urban characteristics I take to be given by the difference

between how w2 changes and how w1 changes (e.g. the difference between the change in the 90-10 differential for w2 and the change in the 90-10 differential forw1).

Similarly, define w3it=Urbanit  ¯ α + ¯δ + ¯βXit+ ¯γUZit+ ¯Uit  + (1−Urbanit)  ˆ αt+ ˆβtXit+ ˆγtRZit+ ¯Rit  (4) and wit4 =Urbanit  ˆ αt+ ˆδt+ ˆβtXit+ ˆγtUZit+ ¯Uit  + (1− Urbanit)  ˆ αt+ ˆβtXit+ ˆγtRZit+ ¯Rit  (5)

where ˆαt, ˆδt, ˆβtand ˆγtrepresent the estimated time- (and with ˆγ, area-) specific coefficients from (1). These two wages are used to compute the contributions of changing rural prices

20These average residuals are computed from the year-specific residuals generated by estimating (1). Based

on an individual’s residual quantile in his own year and area (i.e. urban-rural), I assign to him five residuals - one from each year and the appropriate area. ¯ is simply the average of these five.


(based on changes inw3 relative to w2) and changing urban prices (looking at w4 relative tow3).21 Finally, let w5it=Urbanit  ˆ αt+ ˆδt+ ˆβtXit+ ˆγtUZit+ ¯Uit  + (1− Urbanit)  ˆ αt+ ˆβtXit+ ˆγtRZit+ ˆit  (6) and wit6 =Urbanit  ˆ αt+ ˆδt+ ˆβtXit+ ˆγtUZit+ ˆit  + (1− Urbanit)  ˆ αt+ ˆβtXit+ ˆγtRZit+ ˆit  (7)

where ˆit denotes this individual’s residual from the estimation of (1). Thus,w6it is respon-dent i’s actual log wage in year t. The influence of rural residual elements on inequality is based on the difference between the distributions of w5 and w4; that of urban residual elements is derived from the difference between w6 andw5.22

Results appear in Table 6. In general, they reiterate the basic conclusion already drawn

– namely, that there has been a strong urban component to the rise in overall U.S. wage

inequality. More specifically, we can see that this contribution derives from three important

sources. First, changing urban characteristics at fixed skill prices has added between 3 and

7 percentage points to each decade-by-decade increase in the overall 90-10 wage differential.

21Since these two wage specifications do not isolate the effects of changing skill prices alone –α, δ, and

β are also allowed to be year-specific – I interpret these effects broadly as rural and urban price effects, not simply urban and rural skill price effects.

22It should be noted that this decomposition could also be performed letting urban characteristics and

prices vary first in the sequence (e.g. define w1 using mean rural characteristics but year-specific urban characteristics). Doing so does not greatly alter the conclusions drawn. Only one pair of figures is altered significantly (rural and urban price effects between 1950 and 1960), albeit with the same qualitative outcome.


Again, this result likely reflects the steady rise in the percentage of urban dwellers over

time combined with the higher returns to experience documented in Table 4. Given that

the results also show that the contribution of changing rural characteristics has been mostly

negative (with the exception of the 1970-1980 period, when it was close to zero), the total

characteristic effect on changing inequality has been minor – on the order of 17 to 18 percent

of the total change in the 90-10 difference between 1950 and 1990.

Second, there has been a significant component associated with how those personal

characteristics are priced. Looking at the fourth column of figures in the table, it is apparent

that, with the exception of the 1970-1980 period during which the college premium declined

(see both Tables 3 and 4), changes in urban prices are responsible for roughly 6 to 14

percentage points of each 10-year change in the 90-10 wage difference.23 Since here too, the

rural effect has been primarily negative, contributing roughly -8 percentage points for the

90-10 difference between 1950 and 1990, the total contribution of changing prices – 20 of the

56 percentage points for the 90-10 differential between 1950 and 1990 – tends to understate

the urban price effect which may account for as much as one half of the total rise in the

90-10 gap.

Third, each decade has witnessed a large increase in urban residual inequality. Looking

again at the 90-10 difference, the contribution of urban unobservables has ranged from 2

to 10 percentage points during each decade. The impact of rural residuals has, in contrast,

been considerably smaller, averaging roughly 1 percentage point per decade. So, while in

total, the results reiterate the well-known role played by unobservables in the rise of U.S.

23This latter figure, 14 percentage points between 1950 and 1960, represents the estimate from Table 6

that differs most substantially from what is generated by doing the decomposition using urban prices first (see previous footnote). The other point estimate derived for the urban price estimate in this period is somewhat smaller, although still large and positive: 5.5 percentage points.


wage inequality (i.e. nearly half of the change in the 90-10 differential between 1950 and

1990), we can see that the vast majority of this rise has occurred within cities.24


Concluding Comments

A sizable literature has developed over the past decade arguing that the growing supply of

skills in the U.S. labor market has, in one way or another, contributed to the striking rise

in earnings inequality. Given that skilled workers (or, at least, highly educated workers)

have traditionally been concentrated in urban markets, this notion implies that the rise in

inequality ought to have been particularly pronounced in cities.

This paper has provided evidence in support of this conclusion. To reiterate, although

various measures of wage inequality have risen throughout the entire country, the rise has

been much more substantial within urban areas – defined at either the aggregate U.S.,

region, or individual metropolitan area levels – than in rural ones. The evidence also

indicates that a large part of this difference in inequality can be linked to skill returns,

which tend to be higher in urban areas.

Identifying the specific reasons for these patterns, of course, remains an open question.

To be sure, there are several candidates including the two discussed previously: skill-biased

technological change and increased sorting of high-ability workers into cities. Future work

exploring which of these two (or any other) explanations hold, I believe, would prove useful

on at least two counts: contributing to our understanding of changing wage inequality and

providing greater insight into how urbanization influences labor earnings.


Table 1A: Educational Attainment Distributions – U.S.

Level Category 1950 1960 1970 1980 1990

Urban U.S. Some High School 0.22 0.22 0.19 0.13 0.08

High School 0.26 0.28 0.34 0.35 0.3

Some College 0.09 0.12 0.17 0.21 0.3

College or More 0.08 0.12 0.16 0.23 0.27

Non-Urban U.S. Some High School 0.2 0.21 0.19 0.16 0.11

High School 0.22 0.28 0.36 0.42 0.4

Some College 0.07 0.09 0.13 0.17 0.27

College or More 0.05 0.08 0.1 0.14 0.15


Table 1B: Educational Attainment Distributions – Regions

Level Category 1950 1960 1970 1980 1990

Urban West Some High School 0.22 0.22 0.17 0.11 0.07

High School 0.3 0.3 0.35 0.33 0.26

Some College 0.13 0.16 0.21 0.25 0.33

College or More 0.09 0.13 0.17 0.23 0.26

Non-Urban West Some High School 0.22 0.22 0.17 0.13 0.09

High School 0.24 0.31 0.36 0.39 0.34

Some College 0.1 0.12 0.19 0.23 0.34

College or More 0.07 0.09 0.13 0.18 0.19

Urban Midwest Some High School 0.21 0.22 0.19 0.14 0.08

High School 0.26 0.29 0.36 0.39 0.32

Some College 0.09 0.12 0.16 0.2 0.31

College or More 0.07 0.11 0.15 0.21 0.26

Non-Urban Midwest Some High School 0.18 0.2 0.18 0.14 0.1

High School 0.26 0.32 0.41 0.48 0.43

Some College 0.07 0.1 0.13 0.16 0.28

College or More 0.06 0.08 0.1 0.13 0.14

Urban Northeast Some High School 0.21 0.23 0.2 0.13 0.08

High School 0.25 0.26 0.34 0.37 0.33

Some College 0.06 0.11 0.14 0.19 0.26

College or More 0.08 0.12 0.17 0.24 0.29

Non-Urban Northeast Some High School 0.22 0.22 0.19 0.14 0.1

High School 0.25 0.29 0.38 0.44 0.43

Some College 0.06 0.09 0.13 0.17 0.25

College or More 0.05 0.08 0.12 0.16 0.17

Urban South Some High School 0.23 0.2 0.19 0.14 0.09

High School 0.23 0.28 0.31 0.33 0.29

Some College 0.1 0.13 0.17 0.21 0.3

College or More 0.09 0.13 0.17 0.23 0.27

Non-Urban South Some High School 0.21 0.21 0.21 0.18 0.14

High School 0.15 0.23 0.31 0.37 0.38

Some College 0.06 0.09 0.12 0.15 0.25

College or More 0.05 0.07 0.1 0.13 0.14


Table 2A: Overall Wage Inequality – U.S. Level Measure 1950 1960 1970 1980 1990 Total U.S. 90-10 1.1 1.19 1.31 1.41 1.66 90-50 0.51 0.53 0.57 0.61 0.76 50-10 0.59 0.66 0.75 0.8 0.9 Urban U.S. 90-10 0.96 1.14 1.26 1.43 1.66 90-50 0.49 0.52 0.57 0.6 0.73 50-10 0.47 0.62 0.69 0.83 0.93 Non-Urban U.S. 90-10 1.19 1.24 1.29 1.36 1.5 90-50 0.55 0.52 0.58 0.6 0.68 50-10 0.64 0.73 0.71 0.75 0.82


Table 2B: Overall Wage Inequality – Regions Level Measure 1950 1960 1970 1980 1990 Total West 90-10 1.06 1.18 1.38 1.51 1.75 90-50 0.44 0.49 0.57 0.62 0.78 50-10 0.62 0.69 0.8 0.9 0.97 Urban West 90-10 1.03 1.16 1.4 1.54 1.77 90-50 0.45 0.48 0.58 0.63 0.78 50-10 0.58 0.68 0.82 0.9 0.99 Non-Urban West 90-10 1.12 1.26 1.4 1.43 1.59 90-50 0.46 0.52 0.59 0.61 0.72 50-10 0.66 0.75 0.81 0.81 0.88 Total Midwest 90-10 0.98 1.04 1.13 1.32 1.56 90-50 0.46 0.48 0.51 0.55 0.68 50-10 0.52 0.55 0.62 0.77 0.81 Urban Midwest 90-10 0.91 0.99 1.15 1.3 1.59 90-50 0.44 0.47 0.53 0.55 0.68 50-10 0.47 0.52 0.63 0.75 0.92 Non-Urban Midwest 90-10 1.11 1.07 1.16 1.27 1.44 90-50 0.5 0.47 0.5 0.57 0.65 50-10 0.61 0.6 0.66 0.7 0.8 Total Northeast 90-10 1 1.1 1.2 1.38 1.59 90-50 0.52 0.54 0.61 0.61 0.76 50-10 0.49 0.56 0.59 0.76 0.84 Urban Northeast 90-10 0.96 1.12 1.19 1.39 1.61 90-50 0.52 0.55 0.58 0.61 0.75 50-10 0.44 0.57 0.6 0.78 0.85 Non-Urban Northeast 90-10 1.01 1.08 1.17 1.28 1.43 90-50 0.47 0.47 0.56 0.56 0.65 50-10 0.55 0.61 0.61 0.73 0.78 Total South 90-10 1.25 1.36 1.42 1.45 1.65 90-50 0.57 0.59 0.65 0.68 0.78 50-10 0.68 0.77 0.77 0.77 0.88 Urban South 90-10 1.16 1.31 1.4 1.49 1.72 90-50 0.54 0.58 0.63 0.68 0.82 50-10 0.62 0.72 0.78 0.81 0.9 Non-Urban South 90-10 1.22 1.28 1.37 1.38 1.49 90-50 0.59 0.59 0.65 0.66 0.73 50-10 0.64 0.69 0.72 0.73 0.76


Table 2C: Overall Wage Inequality – Individual Cities

Metropolitan Area Measure 1970 1980 1990

New York 90-10 1.27 1.5 1.71 90-50 0.64 0.69 0.81 50-10 0.64 0.81 0.9 Obs. 26745 30005 30132 Los Angeles 90-10 1.35 1.58 1.89 90-50 0.58 0.66 0.83 50-10 0.77 0.92 1.06 Obs. 18244 20679 24847 Chicago 90-10 1.14 1.28 1.61 90-50 0.53 0.56 0.71 50-10 0.61 0.72 0.9 Obs. 13713 14816 13142 All Others 90-10 1.23 1.35 1.57 90-50 0.57 0.6 0.7 50-10 0.66 0.75 0.86 Obs. 1624.7 1125.9 1136.9

Note: 90-10, 90-50, and 50-10 differences in log weekly wages. Individual cities are defined as (1) New York-Northern New Jersey-Long Island CMSA, (2) Los Angeles-Anaheim-Riverside CMSA, and (3) Chicago-Gary-Lake CMSA. “All Others” gives an average across the in-equality measures of the remaining MSAs and CMSAs. Sample sizes (including New York, Los Angeles, and Chicago) are 104 cities for 1970, 221 for 1980, and 227 for 1990. “Obs.” represents number of individual observations used in the calculations for the first three cities, and the average number for all others. Minimum numbers of individual observations per city in the last case are 343 for 1970, 149 for 1980, and 141 for 1990.


Table 3: Urban and Skill Premia – U.S. Variable 1950 1960 1970 1980 1990 Urban 0.133 0.136 0.14 0.124 0.146 (0.01) (0.01) (0.01) (0.01) (0.01) No High School -0.17 -0.2 -0.21 -0.25 -0.3 (0.01) (0.01) (0.01) (0.016) (0.03)

Some High School -0.07 -0.08 -0.09 -0.12 -0.15

(0.005) (0.004) (0.004) (0.004) (0.007) Some College 0.09 0.095 0.08 0.08 0.125 (0.01) (0.005) (0.004) (0.005) (0.005) College or More 0.251 0.3 0.34 0.32 0.42 (0.01) (0.008) (0.007) (0.006) (0.009) 6-10 Years Exp. 0.19 0.24 0.31 0.22 0.26 (0.008) (0.007) (0.013) (0.005) (0.005) 11-15 Years Exp. 0.28 0.38 0.43 0.36 0.4 (0.008) (0.008) (0.015) (0.006) (0.007) 16-20 Years Exp. 0.34 0.45 0.49 0.45 0.49 (0.01) (0.01) (0.016) (0.008) (0.007) 21-25 Years Exp. 0.38 0.47 0.53 0.5 0.56 (0.01) (0.01) (0.016) (0.009) (0.009) 26-30 Years Exp. 0.4 0.48 0.54 0.51 0.61 (0.01) (0.01) (0.018) (0.01) (0.011) 31-35 Years Exp. 0.41 0.48 0.53 0.51 0.61 (0.015) (0.01) (0.019) (0.01) (0.011) 36-40 Years Exp. 0.41 0.47 0.51 0.5 0.59 (0.015) (0.01) (0.017) (0.01) (0.012) > 40 Years Exp. 0.37 0.46 0.46 0.45 0.54 (0.012) (0.01) (0.018) (0.015) (0.016) R2 0.31 0.38 0.39 0.36 0.38 Individual Obs. 76825 258993 311987 403326 433350

Note: OLS estimates. Dependent variable is log weekly wage. Estimated coefficients on urban, education, and experience indicators. Each regression also includes marital status, weeks worked, and dummies for nine occupations, nine industries, and three Census re-gions. Regressions are performed separately for each year. Standard errors, adjusted for heteroskedasticity and within-state correlation, are reported in parentheses.


Table 4: Skill Premia by Urban Status – U.S. Variable 1950 1960 1970 1980 1990 Urban 0.055 0.06 0.044 0.024 0.034 (0.02) (0.017) (0.02) (0.013) (0.014) No High School -0.18 -0.22 -0.22 -0.22 -0.23 (0.015) (0.017) (0.015) (0.012) (0.017)

Some High School -0.07 -0.09 -0.1 -0.12 -0.14

(0.008) (0.007) (0.007) (0.007) (0.008) Some College 0.08 0.1 0.08 0.07 0.09 (0.012) (0.009) (0.007) (0.005) (0.006) College or More 0.22 0.29 0.32 0.26 0.31 (0.017) (0.012) (0.01) (0.01) (0.012) No High School-Urban 0.01 0.03 0.02 -0.04 -0.08 (0.013) (0.012) (0.014) (0.02) (0.035)

Some High School-Urban -0.001 0.01 0.01 -0.005 -0.01

(0.008) (0.007) (0.007) (0.008) (0.009)

Some College-Urban 0.01 -0.01 0.008 0.02 0.04

(0.012) (0.008) (0.007) (0.007) (0.008)

College or More-Urban 0.04 0.01 0.03 0.08 0.13


Table 4 Continued Variable 1950 1960 1970 1980 1990 6-10 Years Exp. 0.18 0.2 0.27 0.17 0.21 (0.01) (0.011) (0.012) (0.006) (0.008) 11-15 Years Exp. 0.25 0.34 0.38 0.29 0.34 (0.011) (0.013) (0.014) (0.006) (0.009) 16-20 Years Exp. 0.3 0.4 0.43 0.37 0.42 (0.011) (0.014) (0.015) (0.008) (0.01) 21-25 Years Exp. 0.33 0.43 0.47 0.41 0.48 (0.012) (0.014) (0.017) (0.01) (0.01) 26-30 Years Exp. 0.33 0.43 0.46 0.41 0.53 (0.013) (0.015) (0.016) (0.009) (0.012) 31-35 Years Exp. 0.34 0.42 0.44 0.42 0.53 (0.016) (0.016) (0.015) (0.009) (0.014) 36-40 Years Exp. 0.34 0.41 0.42 0.4 0.5 (0.017) (0.017) (0.015) (0.01) (0.011) > 40 Years Exp. 0.3 0.38 0.36 0.33 0.45 (0.013) (0.016) (0.016) (0.012) (0.012) 6-10 Years Exp.-Urban 0.02 0.04 0.06 0.06 0.06 (0.015) (0.013) (0.019) (0.007) (0.009) 11-15 Years Exp.-Urban 0.05 0.05 0.08 0.09 0.07 (0.016) (0.018) (0.019) (0.006) (0.011) 16-20 Years Exp.-Urban 0.06 0.07 0.09 0.11 0.09 (0.017) (0.017) (0.021) (0.008) (0.012) 21-25 Years Exp.-Urban 0.08 0.06 0.08 0.12 0.1 (0.02) (0.018) (0.021) (0.011) (0.011) 26-30 Years Exp.-Urban 0.11 0.07 0.11 0.13 0.1 (0.02) (0.016) (0.02) (0.01) (0.014) 31-35 Years Exp.-Urban 0.11 0.09 0.11 0.12 0.11 (0.023) (0.019) (0.021) (0.011) (0.019) 36-40 Years Exp.-Urban 0.1 0.09 0.12 0.13 0.11 (0.019) (0.018) (0.022) (0.011) (0.015) > 40 Years Exp.-Urban 0.1 0.1 0.14 0.15 0.1 (0.019) (0.018) (0.02) (0.015) (0.019) R2 0.31 0.38 0.39 0.36 0.38

Note: OLS estimates. Dependent variable is log weekly wage. Estimated coefficients on urban, education, and experience indicators; education interactions; and urban-experience interations. Each regression also includes marital status, weeks worked, and dummies for nine occupations, nine industries, and three Census regions. Regressions are performed separately for each year. Standard errors, adjusted for heteroskedasticity and within-state correlation, are reported in parentheses.


Table 5A: Residual Wage Inequality – U.S. Level Measure 1950 1960 1970 1980 1990 Total U.S. 90-10 0.88 0.91 0.98 1.08 1.22 90-50 0.42 0.43 0.46 0.51 0.58 50-10 0.45 0.48 0.51 0.57 0.63 Urban U.S. 90-10 0.85 0.89 0.97 1.08 1.23 90-50 0.41 0.43 0.46 0.51 0.59 50-10 0.43 0.46 0.51 0.57 0.64 Non-Urban U.S. 90-10 0.94 0.95 0.99 1.08 1.18 90-50 0.44 0.44 0.46 0.52 0.56 50-10 0.49 0.5 0.53 0.56 0.62

Note: 90-10, 90-50, and 50-10 differences in residual log weekly wages based on year-specific regressions.


Table 5B: Residual Wage Inequality – Regions Level Measure 1950 1960 1970 1980 1990 Total West 90-10 0.89 0.91 1.01 1.13 1.28 90-50 0.41 0.43 0.47 0.53 0.61 50-10 0.48 0.48 0.54 0.6 0.66 Urban West 90-10 0.87 0.9 1 1.13 1.28 90-50 0.4 0.42 0.47 0.53 0.62 50-10 0.47 0.48 0.53 0.59 0.66 Non-Urban West 90-10 0.92 0.96 1.05 1.15 1.27 90-50 0.41 0.44 0.49 0.54 0.59 50-10 0.51 0.52 0.55 0.61 0.68 Total Midwest 90-10 0.81 0.83 0.91 1.02 1.16 90-50 0.39 0.4 0.44 0.48 0.55 50-10 0.42 0.43 0.48 0.54 0.61 Urban Midwest 90-10 0.78 0.81 0.89 1.01 1.16 90-50 0.38 0.4 0.43 0.47 0.55 50-10 0.4 0.41 0.46 0.53 0.61 Non-Urban Midwest 90-10 0.87 0.87 0.97 1.03 1.16 90-50 0.41 0.39 0.44 0.49 0.54 50-10 0.46 0.46 0.47 0.52 0.61 Total Northeast 90-10 0.84 0.87 0.93 1.03 1.2 90-50 0.42 0.42 0.45 0.49 0.57 50-10 0.41 0.45 0.48 0.54 0.62 Urban Northeast 90-10 0.83 0.87 0.94 1.03 1.21 90-50 0.42 0.42 0.46 0.49 0.58 50-10 0.41 0.45 0.48 0.54 0.63 Non-Urban Northeast 90-10 0.85 0.86 0.91 1.01 1.11 90-50 0.4 0.41 0.43 0.48 0.51 50-10 0.44 0.45 0.48 0.54 0.59 Total South 90-10 0.96 1 1.03 1.11 1.21 90-50 0.47 0.48 0.49 0.53 0.58 50-10 0.49 0.52 0.54 0.58 0.63 Urban South 90-10 0.93 0.99 1.04 1.11 1.23 90-50 0.45 0.47 0.49 0.53 0.59 50-10 0.48 0.52 0.55 0.58 0.64 Non-Urban South 90-10 0.98 1 1.02 1.11 1.18 90-50 0.48 0.49 0.48 0.54 0.57 50-10 0.5 0.51 0.54 0.57 0.61

Note: 90-10, 90-50, and 50-10 differences in residual log weekly wages based on region-year specific regressions.


Table 5C: Residual Wage Inequality – Individual Cities

Metropolitan Area Measure 1970 1980 1990

New York 90-10 1.01 1.11 1.31 90-50 0.49 0.53 0.63 50-10 0.51 0.58 0.67 Los Angeles 90-10 1 1.17 1.33 90-50 0.46 0.55 0.65 50-10 0.54 0.61 0.68 Chicago 90-10 0.92 1.05 1.19 90-50 0.45 0.49 0.56 50-10 0.46 0.56 0.62 All Others 90-10 0.94 1.04 1.17 90-50 0.45 0.49 0.56 50-10 0.49 0.55 0.61

Note: 90-10, 90-50, and 50-10 differences in residual log weekly wages based on year-specific regressions. For additional information, see Table 2C.


Table 6: Inequality Decomposition - Urban Versus Rural Components

90-10 Diff. Rural Urban Rural Urban Rural Urban Total

Characteristics Characteristics Prices Prices Residuals Residuals Change

1950-60 -0.076 0.066 -0.07 0.14 0.01 0.02 0.09

1960-70 -0.03 0.066 -0.033 0.067 0.006 0.05 0.12

1970-80 0.0001 0.073 -0.005 -0.04 0.017 0.056 0.1

1980-90 -0.03 0.03 0.03 0.11 0.01 0.1 0.25

90-50 Diff. Rural Urban Rural Urban Rural Urban Total

Characteristics Characteristics Prices Prices Residuals Residuals Change

1950-60 -0.016 0.005 -0.03 0.05 0.002 0.004 0.015

1960-70 -0.006 0.023 -0.014 0.02 0.004 0.013 0.04

1970-80 -0.006 0.02 -0.007 -0.003 0.005 0.032 0.04

1980-90 -0.004 0.013 0.014 0.066 0.006 0.06 0.155

50-10 Diff. Rural Urban Rural Urban Rural Urban Total

Characteristics Characteristics Prices Prices Residuals Residuals Change

1950-60 -0.06 0.061 -0.037 0.09 0.007 0.016 0.077

1960-70 -0.025 0.043 -0.02 0.046 0.002 0.035 0.08

1970-80 0.006 0.053 0.002 -0.04 0.01 0.024 0.06

1980-90 -0.029 0.02 0.014 0.048 0.008 0.035 0.095

Note: Figures represent decade-by-decade changes in 90-10, 90-50, and 50-10 wage differ-entials associated with observable characteristics, prices of observable characteristics, and unobservables. “Total Change” represents change in overall inequality measure.



Composition of U.S. Census Regions

West: Washington, Oregon, California, Nevada, Idaho, Montana, Wyoming, Utah,

Col-orado, Arizona, New Mexico, Alaska, Hawaii

Midwest: North Dakota, South Dakota, Nebraska, Kansas, Minnesota, Iowa, Missouri,

Wisconsin, Illinois, Michigan, Indiana, Ohio

Northeast: Maine, Vermont, New Hampshire, Massachusetts, Rhode Island, Connecticut,

New York, Pennsylvania, New Jersey

South: Texas, Oklahoma, Arkansas, Louisiana, Kentucky, Tennessee, Mississippi,

Al-abama, West Virginia, Delaware, Maryland, District of Columbia, Virginia, North Carolina, South Carolina, Georgia, Florida



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